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Responsible machine learning (responsible ML)

Develop, use, and govern AI solutions responsibly with Azure AI

Build trusted solutions responsibly with Azure AI

Build responsible AI applications from development to deployment and earn the trust of your customers with machine learning and cognitive services technology.

Responsible development of AI solutions for fairness, reliability, and explainability to deliver trusted outcomes

Responsible usage of AI solutions by applying guidance to optimise performance while minimising harm when deployed

Responsible governance of AI solutions for transparency and accountability to achieve positive outcomes

Assess your machine learning model using the responsible AI dashboard with Azure Machine Learning. Using reproducible and automated workflows, evaluate for model fairness, explainability, error analysis, causal analysis, model performance, and exploratory data analysis.

Make real-life interventions and policies with causal analysis in the responsible AI dashboard. Generate a responsible AI scorecard for trained machine learning models in your Azure Machine Learning workspace at deployment time.

Export the responsible AI scorecard for your machine learning models to a PDF to contextualize responsible AI metrics. Share it with both technical and non-technical audiences to involve stakeholders and streamline compliance review.

Related products

Azure Machine Learning

Use an enterprise-grade service for the end-to-end machine learning lifecycle

Azure Cognitive Services

Add cognitive capabilities to apps with APIs and AI services

Build your machine learning skills with Azure

Learn more about machine learning on Azure and participate in hands-on tutorials with a 30-day learning journey. By the end, you'll be prepared to take the Azure Data Scientist Associate Certification.

Customers using responsible ML

"With Azure Machine Learning and the Responsible AI dashboard, we have the tools we need to understand, refine, and explain our outcomes so we can better serve our patients."

Dr Justin Green, Leadership and Management Fellow at Health Education England North & Orthopedic Surgical Registrar

"We see Azure Machine Learning and our partnership with Microsoft as critical to driving increased adoption and acceptance of AI from the regulators."

Alex Mohelsky, Partner and Advisory Data, Analytic, and AI Leader, EY Canada
Ernst & Young

"With model interpretability in Azure Machine Learning, we have a high degree of confidence that our machine learning model is generating meaningful and fair results."

Daniel Engberg, Head of Data Analytics and Artificial Intelligence, Scandinavian Airlines
Scandinavian Airlines

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